Generative AI for Autonomous Driving: A Review
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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arXiv:2505.08854v2 Announce Type: replace Abstract: Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled ca...
The paper introduces a layered evaluation protocol for generative scenario models used in autonomous driving, focusing on physical consistency and plausibility. It examines internal representations through kinematic alignment, statistical baseline comparison, latent controllability, and activation analysis, and then tests outputs against vehicle dynamics constraints such as lateral jerk thresholds. The protocol is applied to a VAE-based scenario generator and other generative models, revealing deeper insights than standard output-level metrics.
arXiv:2608. 02289v1 Announce Type: cross Abstract: Realistic and diverse trajectory generation is central to enabling higher levels of vehicle automation.
DGCPath is a Distribution‑Aware Generative Contrastive framework designed for self‑supervised path representation learning. It combines a diffusion‑based view generator, a variational contrastive mechanism that aligns latent features at the distribution level, and a generative cross‑supervision module for view‑level consistency. Experiments on three real‑world trajectory datasets show that DGCPath surpasses state‑of‑the‑art baselines on two downstream tasks, indicating stronger generalization and representation effectiveness.
arXiv:2606. 06219v1 Announce Type: cross Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints.
DriveWLA is a unified World‑Language‑Action model that integrates semantic understanding, future scene dynamics, and trajectory generation for autonomous driving. It employs a Mixture‑of‑Transformers architecture with three experts—World, Language, and Action—connected by Cross‑Expert Attention to align semantic and dynamic representations. After reinforcement learning refinement, DriveWLA outperforms existing VLM‑based and world‑model‑based planners on NAVSIM benchmarks and generalizes well to other datasets without fine‑tuning.